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Machine learning-based urban noise appropriateness evaluation method and driving factor analysis.

PloS one
The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and appl...

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

PloS one
Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memo...

Advanced susceptibility analysis of ground deformation disasters using large language models and machine learning: A Hangzhou City case study.

PloS one
To address the prevailing scenario where comprehensive susceptibility assessments of ground deformation disasters primarily rely on knowledge-driven models, with weight judgments largely founded on expert subjective assessments, this study initially ...

Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory.

PloS one
Land use in urban agglomerations is the main source of carbon emissions, and reducing them and improving land use efficiency are the keys to achieving sustainable development goals (SDGs). To advance the literature on densely populated cities and hig...

Utilizing Artificial neural networks (ANN) to regulate Smart cities for sustainable Urban Development and Safeguarding Citizen rights.

Scientific reports
The advent of smart cities has brought about a paradigm shift in urban management and citizen engagement. By leveraging technological advancements, cities are now able to collect and analyze extensive data to optimize service delivery, allocate resou...

Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning.

Environmental geochemistry and health
The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM, SO, NO, and CO, across the Mashhad metropolis from 2017 to 2021. In tandem, it explored their intricate correlations with meteorological condi...

Mapping of high-resolution daily particulate matter (PM) concentration at the city level through a machine learning-based downscaling approach.

Environmental monitoring and assessment
PM pollution is a major global concern, especially in Vietnam, due to its harmful effects on health and the environment. Monitoring local PM levels is crucial for assessing air quality. However, Vietnam's state-of-the-art (SOTA) dataset with a 3 km r...

Does smart city construction promote urban green development? Evidence from a double machine learning model.

Journal of environmental management
The issue of whether smart city construction (SCC) can promote urban green development (UGD) is controversial. To address this problem, first, a UGD evaluation index system with four dimensions, namely, green production, green living, green growth, a...

Unveiling the effects of artificial intelligence and green technology convergence on carbon emissions: An explainable machine learning-based approach.

Journal of environmental management
Green technology and artificial intelligence (AI) are playing a positive role in reducing carbon emissions. Technology convergence, as a typical form of technological innovation, can expedite the realization of low-carbon goals through the outcomes o...

Turning trash into treasure: Exploring the potential of AI in municipal waste management - An in-depth review and future prospects.

Journal of environmental management
Rapid urbanization, economic expansion, and population growth have increased waste generation in many nations worldwide. Research on municipal waste management (MWM) is moving towards new frontiers in efficiency and applicability due to the growing a...